• Dr A Velayudham
• Chinthalapalli Mythili
• Dhanala Susmitha
• Easha Sri G.S
• Kesam Achyutha

In response to the escalating global health concern posed by dengue fever, this project presents an innovative solution for early detection by leveraging cutting-edge technologies. This research proposes a comprehensive system for early dengue detection by integrating sensors, a K-Nearest Neighbors (KNN) and cloud computing. Utilizing a Max30100 for spo2 levels, PPG sensor for pressure, a pH sensor for sweat analysis, and a DHT sensor for temperature, data is collected by the NodeMCU (ESP8266) and transmitted to the cloud. In the cloud, a pre-trained KNN model processes the sensor data to predict the likelihood of dengue based on historical patterns. The results are then relayed back to the NodeMCU, where they are displayed in real-time.This innovative approach harnesses the power of Internet of Things (IoT), machine learning, and cloud computing to enable efficient and timely dengue detection, fostering potential advancements in early intervention and public health monitoring.

DEEP LEARNING APPROACH FOR DENGUE FEVER PREDICTION

"DEEP LEARNING APPROACH FOR DENGUE FEVER PREDICTION", International Journal of Emerging Technologies and Innovative Research (www.jetir.org), ISSN:2349-5162, Vol.11, Issue 4, page no.f147-f154, April-2024, Available :http://www.jetir.org/papers/JETIR2404518.pdf

"DEEP LEARNING APPROACH FOR DENGUE FEVER PREDICTION", International Journal of Emerging Technologies and Innovative Research (www.jetir.org | UGC and issn Approved), ISSN:2349-5162, Vol.11, Issue 4, page no. ppf147-f154, April-2024, Available at : http://www.jetir.org/papers/JETIR2404518.pdf